phish
PHISH in MESH: Korean Adversarial Phonetic Substitution and Phonetic-Semantic Feature Integration Defense
Kim, Byungjun, Kim, Minju, Park, Hyeonchu, Kim, Bugeun
As malicious users increasingly employ phonetic substitution to evade hate speech detection, researchers have investigated such strategies. However, two key challenges remain. First, existing studies have overlooked the Korean language, despite its vulnerability to phonetic perturbations due to its phonographic nature. Second, prior work has primarily focused on constructing datasets rather than developing architectural defenses. To address these challenges, we propose (1) PHonetic-Informed Substitution for Hangul (PHISH) that exploits the phonological characteristics of the Korean writing system, and (2) Mixed Encoding of Semantic-pHonetic features (MESH) that enhances the detector's robustness by incorporating phonetic information at the architectural level. Our experimental results demonstrate the effectiveness of our proposed methods on both perturbed and unperturbed datasets, suggesting that they not only improve detection performance but also reflect realistic adversarial behaviors employed by malicious users.
How AI and machine learning are changing the phishing game
Learn how your company can create applications to automate tasks and generate further efficiencies through low-code/no-code tools on November 9 at the virtual Low-Code/No-Code Summit. Bad actors have learned: The more data they're able to harvest about you, the more likely they'll be able to successfully phish you. Which is probably why this attack vector has never been more popular. Proofpoint's 2022 State of the Phish report revealed that 83% of organizations suffered a successful email-based phishing attack in 2021, a 46% increase compared to 2020. Seventy-eight percent of companies faced a ransomware attack that was propagated from a phishing email, while 86% of businesses experienced bulk phishing attacks and 77% sustained business email compromise (BEC) attacks.
How to win (or at least not lose) the war on phishing? Enlist machine learning
It's Friday, August 3, and I have hooked a live one. Using StreamingPhish, a tool that identifies potential phishing sites by mining data on newly registered certificates, I've spotted an Apple phishing site before it's even ready for victims. Conveniently, the operator has even left a Web shell wide open for me to watch him at work. As I download the phishing kit, I take a look at the site access logs from within the shell. Evidently, I've caught the site just a few hours after the certificate was registered.